BACKGROUND: Early-life stressful experiences are associated with increased risk of adverse psychological outcomes in later life. However, much less is known about associations between early-life positive experiences, such as participation in cognitively stimulating activities, and late-life mental health. We investigated whether greater engagement in cognitively stimulating activities in early life is associated with lower risk of depression and anxiety in late life.

METHODS: We surveyed former participants of the St. Louis Baby Tooth study, between 22 June 2021 and 25 March 2022 to collect information on participants' current depression/anxiety symptoms and their early-life activities ( = 2187 responded). A composite activity score was created to represent the early-life activity level by averaging the frequency of self-reported participation in common cognitively stimulating activities in participants' early life (age 6, 12, 18), each rated on a 1 (least frequent) to 5 (most frequent) point scale. Depression/anxiety symptoms were measured by Patient Health Questionnaire (PHQ-9) and Generalized Anxiety Disorder Screener (GAD-7). We used logistic regressions to estimate odds ratios (OR) and 95% confidence intervals (CI) of outcome risk associated with frequency of early-life activity.

RESULTS: Each one-point increase in the early-life composite cognitive activity score was associated with an OR of 0.54 (95% CI 0.38-0.77) for late-life depression and an OR of 0.94 (95% CI 0.61-1.43) for late-life anxiety, adjusting for age, sex, race, parental education, childhood family structure, and socioeconomic status.

CONCLUSIONS: More frequent participation in cognitively stimulating activities during early life was associated with reduced risk of late-life depression.

Distributed lag models (DLMs) are often used to estimate lagged associations and identify critical exposure windows. In a simulation study of prenatal nitrogen dioxide (NO2) exposure and birth weight, we demonstrate that bias amplification and variance inflation can manifest under certain combinations of DLM estimation approaches and time-trend adjustment methods when using low-spatial-resolution exposures with extended lags. Our simulations showed that when using high-spatial-resolution exposure data, any time-trend adjustment method produced low bias and nominal coverage for the distributed lag estimator. When using either low- or no-spatial-resolution exposures, bias due to time trends was amplified for all adjustment methods. Variance inflation was higher in low- or no-spatial-resolution DLMs when using a long-term spline to adjust for seasonality and long-term trends due to concurvity between a distributed lag function and secular function of time. NO2-birth weight analyses in a Massachusetts-based cohort showed that associations were negative for exposures experienced in gestational weeks 15-30 when using high-spatial-resolution DLMs; however, associations were null and positive for DLMs with low- and no-spatial-resolution exposures, respectively, which is likely due to bias amplification. DLM analyses should jointly consider the spatial resolution of exposure data and the parameterizations of the time trend adjustment and lag constraints.

Organic capping agents are a ubiquitous and crucial part of preparing reproducible and homogeneous batches of nanomaterials, particularly nanocrystals with well-defined facets. Despite studies reporting surface ligands (e.g., capping agents) having a non-negligible role in catalytic behavior, their impact is less understood in contaminant adsorption, an important consideration given their potential to obfuscate facet-dependent trends in performance. To ascribe observed behaviors to the facet or the ligand, this report evaluates the impact of poly(-vinyl-2-pyrrolidone) (PVP), a commonly utilized capping agent, on the adsorption performance of nanohematite particles of varying prevailing facet in the removal of selenite (Se(IV)) as a model system. The PVP capping agent reduces the available surface area for contaminant binding, thus resulting in a reduction in overall Se(IV) adsorbed. However, accounting for the effects of surface area, {012}-faceted nanohematite demonstrates a significantly higher sorption capacity for Se(IV) compared with that of {001}-faceted nanohematite. Notably, chemical treatment is minimally effective in removing strongly bound PVP, indicating that complete removal of surface ligands remains challenging.

Development of novel adsorbents often neglects the competitive adsorption between co-occurring oxo-anions, overestimating realistic pollutant removal potentials, and overlooking the need to improve selectivity of materials. This critical review focuses on adsorptive competition between commonly co-occurring oxo-anions in water and mechanistic approaches for the design and development of selective adsorbents. Six "target" oxo-anion pollutants (arsenate, arsenite, selenate, selenite, chromate, and perchlorate) were selected for study. Five "competing" co-occurring oxo-anions (phosphate, sulfate, bicarbonate, silicate, and nitrate) were selected due to their potential to compete with target oxo-anions for sorption sites resulting in decreased removal of the target oxo-anions. First, a comprehensive review of competition between target and competitor oxo-anions to sorb on commonly used, nonselective, metal (hydr)oxide materials is presented, and the strength of competition between each target and competitive oxo-anion pair is classified. This is followed by a critical discussion of the different equations and models used to quantify selectivity. Next, four mechanisms that have been successfully utilized in the development of selective adsorbents are reviewed: variation in surface complexation, Lewis acid/base hardness, steric hindrance, and electrostatic interactions. For each mechanism, the oxo-anions, both target and competitors, are ranked in terms of adsorptive attraction and technologies that exploit this mechanism are reviewed. Third, given the significant effort to evaluate these systems empirically, the potential to use computational quantum techniques, such as density functional theory (DFT), for modeling and prediction is explored. Finally, areas within the field of selective adsorption requiring further research are detailed with guidance on priorities for screening and defining selective adsorbents.